korrents

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An optimizer that is invariant to rescaling the gradients and cheap in memory is what makes very large models practical to train.

Drawn from what Jimmy Ba and Diederik P. Kingma said

optimizers
What this subject means

optimizers The algorithms that actually move a model's weights during training, and the trade-offs between them.

What they actually said

  1. Jimmy Ba Associate professor at the University of Toronto The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org · 22 Dec 2014All 8 korrents from this piece
    The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters.
  2. Diederik P. Kingma Machine learning researcher The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org · 22 Dec 2014All 8 korrents from this piece
    The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters.

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